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RNA

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Combining gene ontology with deep neural networks to enhance the clustering of single cell RNA-Seq data.

BMC bioinformatics
BACKGROUND: Single cell RNA sequencing (scRNA-seq) is applied to assay the individual transcriptomes of large numbers of cells. The gene expression at single-cell level provides an opportunity for better understanding of cell function and new discove...

Robotic assisted generation of 2'-deoxy-2'-fluoro-modifed RNA aptamers - High performance enabling strategies in aptamer selection.

Methods (San Diego, Calif.)
Aptamer selection is a laborious procedure, requiring expertise and significant resources. These characteristics limit the accessibility of researchers to these molecular tools. We describe a selection procedure, making use of a robotic system that a...

Pre-Treatment Biomarkers of Anti-Tumour Necrosis Factor Therapy Response in Crohn's Disease-A Systematic Review and Gene Ontology Analysis.

Cells
The most prominent treatment for the serious cases of Crohn's disease (CD) are biological tumour necrosis factor (TNF) inhibitors. Unfortunately, therapy nonresponse is still a serious issue in ~1/3 of CD patients. Accurate prediction of responsivene...

Recent methodology progress of deep learning for RNA-protein interaction prediction.

Wiley interdisciplinary reviews. RNA
Interactions between RNAs and proteins play essential roles in many important biological processes. Benefitting from the advances of next generation sequencing technologies, hundreds of RNA-binding proteins (RBP) and their associated RNAs have been r...

MLSeq: Machine learning interface for RNA-sequencing data.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: In the last decade, RNA-sequencing technology has become method-of-choice and prefered to microarray technology for gene expression based classification and differential expression analysis since it produces less noisy data....

RNA-Protein Binding Sites Prediction via Multi Scale Convolutional Gated Recurrent Unit Networks.

IEEE/ACM transactions on computational biology and bioinformatics
RNA-Protein binding plays important roles in the field of gene expression. With the development of high throughput sequencing, several conventional methods and deep learning-based methods have been proposed to predict the binding preference of RNA-pr...

Deep-learning augmented RNA-seq analysis of transcript splicing.

Nature methods
A major limitation of RNA sequencing (RNA-seq) analysis of alternative splicing is its reliance on high sequencing coverage. We report DARTS (https://github.com/Xinglab/DARTS), a computational framework that integrates deep-learning-based predictions...

EPAI-NC: Enhanced prediction of adenosine to inosine RNA editing sites using nucleotide compositions.

Analytical biochemistry
RNA editing process like Adenosine to Intosine (A-to-I) often influences basic functions like splicing stability and most importantly the translation. Thus knowledge about editing sites is of great importance in molecular biology. With the growth of ...

DeepM6ASeq: prediction and characterization of m6A-containing sequences using deep learning.

BMC bioinformatics
BACKGROUND: N6-methyladensine (m6A) is a common and abundant RNA methylation modification found in various species. As a type of post-transcriptional methylation, m6A plays an important role in diverse RNA activities such as alternative splicing, an ...